Relation domain and range completion method based on knowledge graph embedding

被引：0
|
作者：
Lei J.-P. [1 ,2 ]
Ouyang D.-T. [1 ,2 ]
Zhang L.-M. [1 ,2 ]
机构：
[1] College of Computer Science and Technology, Jilin University, Changchun
[2] Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2022年 / 52卷 / 01期
关键词：
Artificial intelligence; Constraints completion; Domain and range; Knowledge graph; Knowledge graph embedding;
D O I：
10.13229/j.cnki.jdxbgxb20200755
中图分类号：
学科分类号：
摘要：
In this paper, we focus on completing the missing domain and range constraints in knowledge graphs and try to predict missing constraints by knowledge graph embedding models. Considering the structure of constraints completion problem, we introduce two efficient approaches, DCaT-T and DCaT-R, which are derived from translation-based knowledge graph embedding models TransE and RotatE. In particular both DCaT-T and DCaT-R exploit a two-stage training approach to improve the performance of the constraints predicting models. Experimental results show that both DCaT-T and DCaT-R are efficient than entity typing approach SDType, DCaT-T performs better than TransE-based entity typing model ConnectE and the two-stage learning approach can improve the performance of the models further. © 2022, Jilin University Press. All right reserved.
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页码：154 / 161
页数：7
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